add:ast-app

Dockerfile

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+FROM jgwill/gix-adaptive-style-transfer:gpu
+
+

LICENSE

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+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

ckp2SavedModel.py

@@ -0,0 +1,14 @@
+import os
+import tensorflow as tf
+
+export_dir = 'export_dir'
+trained_checkpoint_prefix = '/m/wk/1/model_gia-young-picasso-v03-201216-var2_new_240000.ckpt-240000'
+
+#/m/wk/1/model_gia-young-picasso-v03-201216-var2_new_240000.ckpt-240000.data-00000-of-00001
+graph = tf.Graph()
+loader = tf.train.import_meta_graph(trained_checkpoint_prefix + ".meta" )
+sess = tf.Session()
+loader.restore(sess,trained_checkpoint_prefix)
+builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
+builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING], strip_default_attrs=True)
+builder.save()

img_augm.py

@@ -0,0 +1,187 @@
+# Copyright (C) 2018  Artsiom Sanakoyeu and Dmytro Kotovenko
+#
+# This file is part of Adaptive Style Transfer
+#
+# Adaptive Style Transfer is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Adaptive Style Transfer is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+import numpy as np
+import scipy.misc
+import cv2
+from PIL import Image
+
+
+class Augmentor():
+    def __init__(self,
+                 crop_size=(256, 256),
+                 scale_augm_prb=0.5, scale_augm_range=0.2,
+                 rotation_augm_prb=0.5, rotation_augm_range=0.15,
+                 hsv_augm_prb=1.0,
+                 hue_augm_shift=0.05,
+                 saturation_augm_shift=0.05, saturation_augm_scale=0.05,
+                 value_augm_shift=0.05, value_augm_scale=0.05,
+                 affine_trnsfm_prb=0.5, affine_trnsfm_range=0.05,
+                 horizontal_flip_prb=0.5,
+                 vertical_flip_prb=0.5):
+
+        self.crop_size = crop_size
+
+        self.scale_augm_prb = scale_augm_prb
+        self.scale_augm_range = scale_augm_range
+
+        self.rotation_augm_prb = rotation_augm_prb
+        self.rotation_augm_range = rotation_augm_range
+
+        self.hsv_augm_prb = hsv_augm_prb
+        self.hue_augm_shift = hue_augm_shift
+        self.saturation_augm_scale = saturation_augm_scale
+        self.saturation_augm_shift = saturation_augm_shift
+        self.value_augm_scale = value_augm_scale
+        self.value_augm_shift = value_augm_shift
+
+        self.affine_trnsfm_prb = affine_trnsfm_prb
+        self.affine_trnsfm_range = affine_trnsfm_range
+
+        self.horizontal_flip_prb = horizontal_flip_prb
+        self.vertical_flip_prb = vertical_flip_prb
+
+    def __call__(self, image, is_inference=False):
+        if is_inference:
+            return cv2.resize(image, None, fx=self.crop_size[0], fy=self.crop_size[1], interpolation=cv2.INTER_CUBIC)
+
+        # If not inference stage apply the pipeline of augmentations.
+        if self.scale_augm_prb > np.random.uniform():
+            image = self.scale(image=image,
+                               scale_x=1. + np.random.uniform(low=-self.scale_augm_range, high=-self.scale_augm_range),
+                               scale_y=1. + np.random.uniform(low=-self.scale_augm_range, high=-self.scale_augm_range)
+                               )
+
+
+        rows, cols, ch = image.shape
+        image = np.pad(array=image, pad_width=[[rows // 4, rows // 4], [cols // 4, cols // 4], [0, 0]], mode='reflect')
+        if self.rotation_augm_prb > np.random.uniform():
+            image = self.rotate(image=image,
+                                angle=np.random.uniform(low=-self.rotation_augm_range*90.,
+                                                        high=self.rotation_augm_range*90.)
+                                )
+
+        if self.affine_trnsfm_prb > np.random.uniform():
+            image = self.affine(image=image,
+                                rng=self.affine_trnsfm_range
+                                )
+        image = image[(rows // 4):-(rows // 4), (cols // 4):-(cols // 4), :]
+
+        # Crop out patch of desired size.
+        image = self.crop(image=image,
+                          crop_size=self.crop_size
+                          )
+
+        if self.hsv_augm_prb > np.random.uniform():
+            image = self.hsv_transform(image=image,
+                                       hue_shift=self.hue_augm_shift,
+                                       saturation_shift=self.saturation_augm_shift,
+                                       saturation_scale=self.saturation_augm_scale,
+                                       value_shift=self.value_augm_shift,
+                                       value_scale=self.value_augm_scale)
+
+        if self.horizontal_flip_prb > np.random.uniform():
+            image = self.horizontal_flip(image)
+
+        if self.vertical_flip_prb > np.random.uniform():
+            image = self.vertical_flip(image)
+
+        return image
+
+    def scale(self, image, scale_x, scale_y):
+        """
+        Args:
+            image:
+            scale_x: float positive value. New horizontal scale
+            scale_y: float positive value. New vertical scale
+        Returns:
+        """
+        image = cv2.resize(image, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_CUBIC)
+        return image
+
+    def rotate(self, image, angle):
+        """
+        Args:
+            image: input image
+            angle: angle of rotation in degrees
+        Returns:
+        """
+        rows, cols, ch = image.shape
+
+        rot_M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)
+        image = cv2.warpAffine(image, rot_M, (cols, rows))
+        return image
+
+    def crop(self, image, crop_size=(256, 256)):
+        rows, cols, chs = image.shape
+        x = int(np.random.uniform(low=0, high=max(0, rows - crop_size[0])))
+        y = int(np.random.uniform(low=0, high=max(0, cols - crop_size[1])))
+
+        image = image[x:x+crop_size[0], y:y+crop_size[1], :]
+        # If the input image was too small to comprise patch of size crop_size,
+        # resize obtained patch to desired size.
+        if image.shape[0] < crop_size[0] or image.shape[1] < crop_size[1]:
+            image = scipy.misc.imresize(arr=image, size=crop_size)
+        return image
+
+    def hsv_transform(self, image,
+                      hue_shift=0.2,
+                      saturation_shift=0.2, saturation_scale=0.2,
+                      value_shift=0.2, value_scale=0.2,
+                      ):
+
+        image = Image.fromarray(image)
+        hsv = np.array(image.convert("HSV"), 'float64')
+
+        # scale the values to fit between 0 and 1
+        hsv /= 255.
+
+        # do the scalings & shiftings
+        hsv[..., 0] += np.random.uniform(-hue_shift, hue_shift)
+        hsv[..., 1] *= np.random.uniform(1. / (1. + saturation_scale), 1. + saturation_scale)
+        hsv[..., 1] += np.random.uniform(-saturation_shift, saturation_shift)
+        hsv[..., 2] *= np.random.uniform(1. / (1. + value_scale), 1. + value_scale)
+        hsv[..., 2] += np.random.uniform(-value_shift, value_shift)
+
+        # cut off invalid values
+        hsv.clip(0.01, 0.99, hsv)
+
+        # round to full numbers
+        hsv = np.uint8(np.round(hsv * 254.))
+
+        # convert back to rgb image
+        return np.asarray(Image.fromarray(hsv, "HSV").convert("RGB"))
+
+
+    def affine(self, image, rng):
+        rows, cols, ch = image.shape
+        pts1 = np.float32([[0., 0.], [0., 1.], [1., 0.]])
+        [x0, y0] = [0. + np.random.uniform(low=-rng, high=rng), 0. + np.random.uniform(low=-rng, high=rng)]
+        [x1, y1] = [0. + np.random.uniform(low=-rng, high=rng), 1. + np.random.uniform(low=-rng, high=rng)]
+        [x2, y2] = [1. + np.random.uniform(low=-rng, high=rng), 0. + np.random.uniform(low=-rng, high=rng)]
+        pts2 = np.float32([[x0, y0], [x1, y1], [x2, y2]])
+        affine_M = cv2.getAffineTransform(pts1, pts2)
+        image = cv2.warpAffine(image, affine_M, (cols, rows))
+
+        return image
+
+    def horizontal_flip(self, image):
+        return image[:, ::-1, :]
+
+    def vertical_flip(self, image):
+        return image[::-1, :, :]
+

main.py

@@ -0,0 +1,173 @@
+# Copyright (C) 2018  Artsiom Sanakoyeu and Dmytro Kotovenko
+#
+# This file is part of Adaptive Style Transfer
+#
+# Adaptive Style Transfer is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Adaptive Style Transfer is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+import argparse
+import tensorflow as tf
+tf.set_random_seed(228)
+from model import Artgan
+
+def parse_list(str_value):
+    if ',' in str_value:
+        str_value = str_value.split(',')
+    else:
+        str_value = [str_value]
+    return str_value
+
+
+parser = argparse.ArgumentParser(description='')
+
+# ========================== GENERAL PARAMETERS ========================= #
+parser.add_argument('--model_name',
+                    dest='model_name',
+                    default='model1',
+                    help='Name of the model')
+parser.add_argument('--phase',
+                    dest='phase',
+                    default='train',
+                    help='Specify current phase: train or inference.')
+parser.add_argument('--image_size',
+                    dest='image_size',
+                    type=int,
+                    default=256*3,
+                    help='For training phase: will crop out images of this particular size.'
+                         'For inference phase: each input image will have the smallest side of this size. '
+                         'For inference recommended size is 1280.')
+
+
+# ========================= TRAINING PARAMETERS ========================= #
+parser.add_argument('--ptad',
+                    dest='path_to_art_dataset',
+                    type=str,
+                    #default='./data/vincent-van-gogh_paintings/',
+                    default='./data/vincent-van-gogh_road-with-cypresses-1890',
+                    help='Directory with paintings representing style we want to learn.')
+parser.add_argument('--ptcd',
+                    dest='path_to_content_dataset',
+                    type=str,
+                    default=None,
+                    help='Path to Places365 training dataset.')
+
+
+parser.add_argument('--total_steps',
+                    dest='total_steps',
+                    type=int,
+                    default=int(3e5),
+                    help='Total number of steps')
+
+parser.add_argument('--batch_size',
+                    dest='batch_size',
+                    type=int,
+                    default=1,
+                    help='# images in batch')
+parser.add_argument('--lr',
+                    dest='lr',
+                    type=float,
+                    default=0.0002,
+                    help='initial learning rate for adam')
+parser.add_argument('--save_freq',
+                    dest='save_freq',
+                    type=int,
+                    default=1000,
+                    help='Save model every save_freq steps')
+parser.add_argument('--ngf',
+                    dest='ngf',
+                    type=int,
+                    default=32,
+                    help='Number of filters in first conv layer of generator(encoder-decoder).')
+parser.add_argument('--ndf',
+                    dest='ndf',
+                    type=int,
+                    default=64,
+                    help='Number of filters in first conv layer of discriminator.')
+
+# Weights of different losses.
+parser.add_argument('--dlw',
+                    dest='discr_loss_weight',
+                    type=float,
+                    default=1.,
+                    help='Weight of discriminator loss.')
+parser.add_argument('--tlw',
+                    dest='transformer_loss_weight',
+                    type=float,
+                    default=100.,
+                    help='Weight of transformer loss.')
+parser.add_argument('--flw',
+                    dest='feature_loss_weight',
+                    type=float,
+                    default=100.,
+                    help='Weight of feature loss.')
+parser.add_argument('--dsr',
+                    dest='discr_success_rate',
+                    type=float,
+                    default=0.8,
+                    help='Rate of trials that discriminator will win on average.')
+
+
+# ========================= INFERENCE PARAMETERS ========================= #
+parser.add_argument('--ii_dir',
+                    dest='inference_images_dir',
+                    type=parse_list,
+                    default=['./data/sample_photographs/'],
+                    help='Directory with images we want to process.')
+parser.add_argument('--save_dir',
+                    type=str,
+                    default=None,
+                    help='Directory to save inference output images.'
+                         'If not specified will save in the model directory.')
+parser.add_argument('--file_suffix',
+                    type=str,
+                    default='_stylized',
+                    help='Suffix to append in between ext format and fn.'
+                         'If not specified will save in the model directory.')
+parser.add_argument('--ckpt_nmbr',
+                    dest='ckpt_nmbr',
+                    type=int,
+                    default=None,
+                    help='Checkpoint number we want to use for inference. '
+                         'Might be None(unspecified), then the latest available will be used.')
+
+args = parser.parse_args()
+
+
+def main(_):
+
+    tfconfig = tf.ConfigProto(allow_soft_placement=False)
+    tfconfig.gpu_options.allow_growth = True
+    with tf.Session(config=tfconfig) as sess:
+        model = Artgan(sess, args)
+
+        if args.phase == 'train':
+            model.train(args, ckpt_nmbr=args.ckpt_nmbr)
+        if args.phase == 'inference' or args.phase == 'test':
+            print("Inference.")
+            model.inference(args, args.inference_images_dir, resize_to_original=False,
+                            to_save_dir=args.save_dir,
+                            ckpt_nmbr=args.ckpt_nmbr,
+                            file_suffix=args.file_suffix)
+
+        if args.phase == 'inference_on_frames' or args.phase == 'test_on_frames':
+            print("Inference on frames sequence.")
+            model.inference_video(args,
+                                  path_to_folder=args.inference_images_dir[0],
+                                  resize_to_original=False,
+                                  to_save_dir=args.save_dir,
+                                  ckpt_nmbr = args.ckpt_nmbr,
+                                  file_suffix=args.file_suffix)
+        sess.close()
+
+if __name__ == '__main__':
+    tf.app.run()

model.py

@@ -0,0 +1,555 @@
+# Copyright (C) 2018  Artsiom Sanakoyeu and Dmytro Kotovenko
+#
+# This file is part of Adaptive Style Transfer
+#
+# Adaptive Style Transfer is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Adaptive Style Transfer is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+from glob import glob
+import tensorflow as tf
+import numpy as np
+from collections import namedtuple
+from tqdm import tqdm
+import multiprocessing
+
+from module import *
+from utils import *
+import prepare_dataset
+import img_augm
+
+
+class Artgan(object):
+    def __init__(self, sess, args):
+        self.model_name = args.model_name
+        self.root_dir = './models'
+        self.checkpoint_dir = os.path.join(self.root_dir, self.model_name, 'checkpoint')
+        self.checkpoint_long_dir = os.path.join(self.root_dir, self.model_name, 'checkpoint_long')
+        self.sample_dir = os.path.join(self.root_dir, self.model_name, 'sample')
+        self.inference_dir = os.path.join(self.root_dir, self.model_name, 'inference')
+        self.logs_dir = os.path.join(self.root_dir, self.model_name, 'logs')
+
+        self.sess = sess
+        self.batch_size = args.batch_size
+        self.image_size = args.image_size
+
+        self.loss = sce_criterion
+
+        self.initial_step = 0
+
+        OPTIONS = namedtuple('OPTIONS',
+                             'batch_size image_size \
+                              total_steps save_freq lr\
+                              gf_dim df_dim \
+                              is_training \
+                              path_to_content_dataset \
+                              path_to_art_dataset \
+                              discr_loss_weight transformer_loss_weight feature_loss_weight')
+        self.options = OPTIONS._make((args.batch_size, args.image_size,
+                                      args.total_steps, args.save_freq, args.lr,
+                                      args.ngf, args.ndf,
+                                      args.phase == 'train',
+                                      args.path_to_content_dataset,
+                                      args.path_to_art_dataset,
+                                      args.discr_loss_weight, args.transformer_loss_weight, args.feature_loss_weight
+                                      ))
+
+        # Create all the folders for saving the model
+        if not os.path.exists(self.root_dir):
+            os.makedirs(self.root_dir)
+        if not os.path.exists(os.path.join(self.root_dir, self.model_name)):
+            os.makedirs(os.path.join(self.root_dir, self.model_name))
+        if not os.path.exists(self.checkpoint_dir):
+            os.makedirs(self.checkpoint_dir)
+        if not os.path.exists(self.checkpoint_long_dir):
+            os.makedirs(self.checkpoint_long_dir)
+        if not os.path.exists(self.sample_dir):
+            os.makedirs(self.sample_dir)
+        if not os.path.exists(self.inference_dir):
+            os.makedirs(self.inference_dir)
+
+        self._build_model()
+        #@STCGoal Keep an entire sequence of each 1000 iterations steps
+        #@q Do that bellow set to 405 would keep the whole sequence ??
+        self.saver = tf.train.Saver(max_to_keep=2)
+        self.saver_long = tf.train.Saver(max_to_keep=None)
+
+    def _build_model(self):
+        if self.options.is_training:
+            # ==================== Define placeholders. ===================== #
+            with tf.name_scope('placeholder'):
+                self.input_painting = tf.placeholder(dtype=tf.float32,
+                                                     shape=[self.batch_size, None, None, 3],
+                                                     name='painting')
+                self.input_photo = tf.placeholder(dtype=tf.float32,
+                                                  shape=[self.batch_size, None, None, 3],
+                                                  name='photo')
+                self.lr = tf.placeholder(dtype=tf.float32, shape=(), name='learning_rate')
+
+            # ===================== Wire the graph. ========================= #
+            # Encode input images.
+            self.input_photo_features = encoder(image=self.input_photo,
+                                                options=self.options,
+                                                reuse=False)
+
+            # Decode obtained features
+            self.output_photo = decoder(features=self.input_photo_features,
+                                        options=self.options,
+                                        reuse=False)
+
+            # Get features of output images. Need them to compute feature loss.
+            self.output_photo_features = encoder(image=self.output_photo,
+                                                 options=self.options,
+                                                 reuse=True)
+
+            # Add discriminators.
+            # Note that each of the predictions contain multiple predictions
+            # at different scale.
+            self.input_painting_discr_predictions = discriminator(image=self.input_painting,
+                                                                  options=self.options,
+                                                                  reuse=False)
+            self.input_photo_discr_predictions = discriminator(image=self.input_photo,
+                                                               options=self.options,
+                                                               reuse=True)
+            self.output_photo_discr_predictions = discriminator(image=self.output_photo,
+                                                                options=self.options,
+                                                                reuse=True)
+
+            # ===================== Final losses that we optimize. ===================== #
+
+            # Discriminator.
+            # Have to predict ones only for original paintings, otherwise predict zero.
+            scale_weight = {"scale_0": 1.,
+                            "scale_1": 1.,
+                            "scale_3": 1.,
+                            "scale_5": 1.,
+                            "scale_6": 1.}
+            self.input_painting_discr_loss = {key: self.loss(pred, tf.ones_like(pred)) * scale_weight[key]
+                                              for key, pred in zip(self.input_painting_discr_predictions.keys(),
+                                                                   self.input_painting_discr_predictions.values())}
+            self.input_photo_discr_loss = {key: self.loss(pred, tf.zeros_like(pred)) * scale_weight[key]
+                                           for key, pred in zip(self.input_photo_discr_predictions.keys(),
+                                                                self.input_photo_discr_predictions.values())}
+            self.output_photo_discr_loss = {key: self.loss(pred, tf.zeros_like(pred)) * scale_weight[key]
+                                            for key, pred in zip(self.output_photo_discr_predictions.keys(),
+                                                                 self.output_photo_discr_predictions.values())}
+
+            self.discr_loss = tf.add_n(list(self.input_painting_discr_loss.values())) + \
+                              tf.add_n(list(self.input_photo_discr_loss.values())) + \
+                              tf.add_n(list(self.output_photo_discr_loss.values()))
+
+            # Compute discriminator accuracies.
+            self.input_painting_discr_acc = {key: tf.reduce_mean(tf.cast(x=(pred > tf.zeros_like(pred)),
+                                                                         dtype=tf.float32)) * scale_weight[key]
+                                             for key, pred in zip(self.input_painting_discr_predictions.keys(),
+                                                                  self.input_painting_discr_predictions.values())}
+            self.input_photo_discr_acc = {key: tf.reduce_mean(tf.cast(x=(pred < tf.zeros_like(pred)),
+                                                                      dtype=tf.float32)) * scale_weight[key]
+                                          for key, pred in zip(self.input_photo_discr_predictions.keys(),
+                                                               self.input_photo_discr_predictions.values())}
+            self.output_photo_discr_acc = {key: tf.reduce_mean(tf.cast(x=(pred < tf.zeros_like(pred)),
+                                                                       dtype=tf.float32)) * scale_weight[key]
+                                           for key, pred in zip(self.output_photo_discr_predictions.keys(),
+                                                                self.output_photo_discr_predictions.values())}
+            self.discr_acc = (tf.add_n(list(self.input_painting_discr_acc.values())) + \
+                              tf.add_n(list(self.input_photo_discr_acc.values())) + \
+                              tf.add_n(list(self.output_photo_discr_acc.values()))) / float(len(scale_weight.keys())*3)
+
+
+            # Generator.
+            # Predicts ones for both output images.
+            self.output_photo_gener_loss = {key: self.loss(pred, tf.ones_like(pred)) * scale_weight[key]
+                                            for key, pred in zip(self.output_photo_discr_predictions.keys(),
+                                                                 self.output_photo_discr_predictions.values())}
+
+            self.gener_loss = tf.add_n(list(self.output_photo_gener_loss.values()))
+
+            # Compute generator accuracies.
+            self.output_photo_gener_acc = {key: tf.reduce_mean(tf.cast(x=(pred > tf.zeros_like(pred)),
+                                                                       dtype=tf.float32)) * scale_weight[key]
+                                           for key, pred in zip(self.output_photo_discr_predictions.keys(),
+                                                                self.output_photo_discr_predictions.values())}
+
+            self.gener_acc = tf.add_n(list(self.output_photo_gener_acc.values())) / float(len(scale_weight.keys()))
+
+
+            # Image loss.
+            self.img_loss_photo = mse_criterion(transformer_block(self.output_photo),
+                                                transformer_block(self.input_photo))
+            self.img_loss = self.img_loss_photo
+
+            # Features loss.
+            self.feature_loss_photo = abs_criterion(self.output_photo_features, self.input_photo_features)
+            self.feature_loss = self.feature_loss_photo
+
+            # ================== Define optimization steps. =============== #
+            t_vars = tf.trainable_variables()
+            self.discr_vars = [var for var in t_vars if 'discriminator' in var.name]
+            self.encoder_vars = [var for var in t_vars if 'encoder' in var.name]
+            self.decoder_vars = [var for var in t_vars if 'decoder' in var.name]
+
+            # Discriminator and generator steps.
+            update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+
+            with tf.control_dependencies(update_ops):
+                self.d_optim_step = tf.train.AdamOptimizer(self.lr).minimize(
+                    loss=self.options.discr_loss_weight * self.discr_loss,
+                    var_list=[self.discr_vars])
+                self.g_optim_step = tf.train.AdamOptimizer(self.lr).minimize(
+                    loss=self.options.discr_loss_weight * self.gener_loss +
+                         self.options.transformer_loss_weight * self.img_loss +
+                         self.options.feature_loss_weight * self.feature_loss,
+                    var_list=[self.encoder_vars + self.decoder_vars])
+
+            # ============= Write statistics to tensorboard. ================ #
+
+            # Discriminator loss summary.
+            s_d1 = [tf.summary.scalar("discriminator/input_painting_discr_loss/"+key, val)
+                    for key, val in zip(self.input_painting_discr_loss.keys(), self.input_painting_discr_loss.values())]
+            s_d2 = [tf.summary.scalar("discriminator/input_photo_discr_loss/"+key, val)
+                    for key, val in zip(self.input_photo_discr_loss.keys(), self.input_photo_discr_loss.values())]
+            s_d3 = [tf.summary.scalar("discriminator/output_photo_discr_loss/" + key, val)
+                    for key, val in zip(self.output_photo_discr_loss.keys(), self.output_photo_discr_loss.values())]
+            s_d = tf.summary.scalar("discriminator/discr_loss", self.discr_loss)
+            self.summary_discriminator_loss = tf.summary.merge(s_d1+s_d2+s_d3+[s_d])
+
+            # Discriminator acc summary.
+            s_d1_acc = [tf.summary.scalar("discriminator/input_painting_discr_acc/"+key, val)
+                    for key, val in zip(self.input_painting_discr_acc.keys(), self.input_painting_discr_acc.values())]
+            s_d2_acc = [tf.summary.scalar("discriminator/input_photo_discr_acc/"+key, val)
+                    for key, val in zip(self.input_photo_discr_acc.keys(), self.input_photo_discr_acc.values())]
+            s_d3_acc = [tf.summary.scalar("discriminator/output_photo_discr_acc/" + key, val)
+                    for key, val in zip(self.output_photo_discr_acc.keys(), self.output_photo_discr_acc.values())]
+            s_d_acc = tf.summary.scalar("discriminator/discr_acc", self.discr_acc)
+            s_d_acc_g = tf.summary.scalar("discriminator/discr_acc", self.gener_acc)
+            self.summary_discriminator_acc = tf.summary.merge(s_d1_acc+s_d2_acc+s_d3_acc+[s_d_acc])
+
+            # Image loss summary.
+            s_i1 = tf.summary.scalar("image_loss/photo", self.img_loss_photo)
+            s_i = tf.summary.scalar("image_loss/loss", self.img_loss)
+            self.summary_image_loss = tf.summary.merge([s_i1 + s_i])
+
+            # Feature loss summary.
+            s_f1 = tf.summary.scalar("feature_loss/photo", self.feature_loss_photo)
+            s_f = tf.summary.scalar("feature_loss/loss", self.feature_loss)
+            self.summary_feature_loss = tf.summary.merge([s_f1 + s_f])
+
+            self.summary_merged_all = tf.summary.merge_all()
+            self.writer = tf.summary.FileWriter(self.logs_dir, self.sess.graph)
+        else:
+            # ==================== Define placeholders. ===================== #
+            with tf.name_scope('placeholder'):
+                self.input_photo = tf.placeholder(dtype=tf.float32,
+                                                  shape=[self.batch_size, None, None, 3],
+                                                  name='photo')
+
+            # ===================== Wire the graph. ========================= #
+            # Encode input images.
+            self.input_photo_features = encoder(image=self.input_photo,
+                                                options=self.options,
+                                                reuse=False)
+
+            # Decode obtained features.
+            self.output_photo = decoder(features=self.input_photo_features,
+                                        options=self.options,
+                                        reuse=False)
+
+    def train(self, args, ckpt_nmbr=None):
+        # Initialize augmentor.
+        augmentor = img_augm.Augmentor(crop_size=[self.options.image_size, self.options.image_size],
+                                       vertical_flip_prb=0.,
+                                       hsv_augm_prb=1.0,
+                                       hue_augm_shift=0.05,
+                                       saturation_augm_shift=0.05, saturation_augm_scale=0.05,
+                                       value_augm_shift=0.05, value_augm_scale=0.05, )
+        content_dataset_places = prepare_dataset.PlacesDataset(path_to_dataset=self.options.path_to_content_dataset)
+        art_dataset = prepare_dataset.ArtDataset(path_to_art_dataset=self.options.path_to_art_dataset)
+
+
+        # Initialize queue workers for both datasets.
+        q_art = multiprocessing.Queue(maxsize=10)
+        q_content = multiprocessing.Queue(maxsize=10)
+        jobs = []
+        for i in range(5):
+            p = multiprocessing.Process(target=content_dataset_places.initialize_batch_worker,
+                                        args=(q_content, augmentor, self.batch_size, i))
+            p.start()
+            jobs.append(p)
+
+            p = multiprocessing.Process(target=art_dataset.initialize_batch_worker,
+                                        args=(q_art, augmentor, self.batch_size, i))
+            p.start()
+            jobs.append(p)
+        print("Processes are started.")
+        time.sleep(3)
+
+        # Now initialize the graph
+        init_op = tf.global_variables_initializer()
+        self.sess.run(init_op)
+        print("Start training.")
+
+        if self.load(self.checkpoint_dir, ckpt_nmbr):
+            print(" [*] Load SUCCESS")
+        else:
+            if self.load(self.checkpoint_long_dir, ckpt_nmbr):
+                print(" [*] Load SUCCESS")
+            else:
+                print(" [!] Load failed...")
+
+        # Initial discriminator success rate.
+        win_rate = args.discr_success_rate
+        discr_success = args.discr_success_rate
+        alpha = 0.05
+
+        for step in tqdm(range(self.initial_step, self.options.total_steps+1),
+                         initial=self.initial_step,
+                         total=self.options.total_steps):
+            # Get batch from the queue with batches q, if the last is non-empty.
+            while q_art.empty() or q_content.empty():
+                pass
+            batch_art = q_art.get()
+            batch_content = q_content.get()
+
+            if discr_success >= win_rate:
+                # Train generator
+                _, summary_all, gener_acc_ = self.sess.run(
+                    [self.g_optim_step, self.summary_merged_all, self.gener_acc],
+                    feed_dict={
+                        self.input_painting: normalize_arr_of_imgs(batch_art['image']),
+                        self.input_photo: normalize_arr_of_imgs(batch_content['image']),
+                        self.lr: self.options.lr
+                    })
+                discr_success = discr_success * (1. - alpha) + alpha * (1. - gener_acc_)
+            else:
+                # Train discriminator.
+                _, summary_all, discr_acc_ = self.sess.run(
+                    [self.d_optim_step, self.summary_merged_all, self.discr_acc],
+                    feed_dict={
+                        self.input_painting: normalize_arr_of_imgs(batch_art['image']),
+                        self.input_photo: normalize_arr_of_imgs(batch_content['image']),
+                        self.lr: self.options.lr
+                    })
+
+                discr_success = discr_success * (1. - alpha) + alpha * discr_acc_
+            self.writer.add_summary(summary_all, step * self.batch_size)
+
+            if step % self.options.save_freq == 0 and step > self.initial_step:
+                self.save(step)
+
+            # And additionally save all checkpoints each 15000 steps.
+            if step % 15000 == 0 and step > self.initial_step:
+                self.save(step, is_long=True)
+
+            if step % 500 == 0:
+                output_paintings_, output_photos_= self.sess.run(
+                    [self.input_painting, self.output_photo],
+                    feed_dict={
+                        self.input_painting: normalize_arr_of_imgs(batch_art['image']),
+                        self.input_photo: normalize_arr_of_imgs(batch_content['image']),
+                        self.lr: self.options.lr
+                    })
+
+                save_batch(input_painting_batch=batch_art['image'],
+                           input_photo_batch=batch_content['image'],
+                           output_painting_batch=denormalize_arr_of_imgs(output_paintings_),
+                           output_photo_batch=denormalize_arr_of_imgs(output_photos_),
+                           filepath='%s/step_%d.jpg' % (self.sample_dir, step))
+        print("Training is finished. Terminate jobs.")
+        for p in jobs:
+            p.join()
+            p.terminate()
+
+        print("Done.")
+        print("Does the sys.exit() made this process to exit ??")
+        sys.exit()
+
+    # Don't use this function yet.
+    def inference_video(self, args, path_to_folder, to_save_dir=None, resize_to_original=True,
+                        use_time_smooth_randomness=True, ckpt_nmbr=None,file_suffix= "_stylized"):
+        """
+        Run inference on the video frames. Original aspect ratio will be preserved.
+        Args:
+            args:
+            path_to_folder: path to the folder with frames from the video
+            to_save_dir:
+            resize_to_original:
+            use_time_smooth_randomness: change the random vector
+            which is added to the bottleneck features linearly over tim
+
+        Returns:
+
+        """
+        init_op = tf.global_variables_initializer()
+        self.sess.run(init_op)
+        print("Start inference.")
+
+        if self.load(self.checkpoint_dir, ckpt_nmbr):
+            print(" [*] Load SUCCESS")
+        else:
+            if self.load(self.checkpoint_long_dir, ckpt_nmbr):
+                print(" [*] Load SUCCESS")
+            else:
+                print(" [!] Load failed...")
+
+        # Create folder to store results.
+        if to_save_dir is None:
+            to_save_dir = os.path.join(self.root_dir, self.model_name,
+                                       'inference_ckpt%d_sz%d' % (self.initial_step, self.image_size))
+
+        if not os.path.exists(to_save_dir):
+            os.makedirs(to_save_dir)
+
+        image_paths = sorted(os.listdir(path_to_folder))
+        num_images = len(image_paths)
+        for img_idx, img_name in enumerate(tqdm(image_paths)):
+
+            img_path = os.path.join(path_to_folder, img_name)
+            img = scipy.misc.imread(img_path, mode='RGB')
+            img_shape = img.shape[:2]
+            # Prepare image for feeding into network.
+            scale_mult = self.image_size / np.min(img_shape)
+            new_shape = (np.array(img_shape, dtype=float) * scale_mult).astype(int)
+
+            img = scipy.misc.imresize(img, size=new_shape)
+
+            img = np.expand_dims(img, axis=0)
+
+            if use_time_smooth_randomness and img_idx == 0:
+                features_delta = self.sess.run(self.labels_to_concatenate_to_features,
+                               feed_dict={
+                                   self.input_photo: normalize_arr_of_imgs(img),
+                               })
+                features_delta_start = features_delta + np.random.random(size=features_delta.shape) * 0.5 - 0.25
+                features_delta_start = features_delta_start.clip(0, 1000)
+                print('features_delta_start.shape=', features_delta_start.shape)
+                features_delta_end = features_delta + np.random.random(size=features_delta.shape) * 0.5 - 0.25
+                features_delta_end = features_delta_end.clip(0, 1000)
+                step = (features_delta_end - features_delta_start) / (num_images - 1)
+
+            feed_dict = {
+                self.input_painting: normalize_arr_of_imgs(img),
+                self.input_photo: normalize_arr_of_imgs(img),
+                self.lr: self.options.lr
+            }
+            if use_time_smooth_randomness:
+                pass
+
+            img = self.sess.run(self.output_photo, feed_dict=feed_dict)
+
+            img = img[0]
+            img = denormalize_arr_of_imgs(img)
+            if resize_to_original:
+                img = scipy.misc.imresize(img, size=img_shape)
+            else:
+                pass
+
+            scipy.misc.imsave(os.path.join(to_save_dir, img_name[:-4] + file_suffix +".jpg"), img)
+
+        print("Inference is finished.")
+
+    def inference(self, args, path_to_folder, to_save_dir=None, resize_to_original=True,
+                  ckpt_nmbr=None,file_suffix= "_stylized"):
+
+        init_op = tf.global_variables_initializer()
+        self.sess.run(init_op)
+        print("Start inference.")
+
+        if self.load(self.checkpoint_dir, ckpt_nmbr):
+            print(" [*] Load SUCCESS")
+        else:
+            if self.load(self.checkpoint_long_dir, ckpt_nmbr):
+                print(" [*] Load SUCCESS")
+            else:
+                print(" [!] Load failed...")
+                #Exit if we can not load (fix issue inferencing noizy image)
+                sys.exit()
+
+        # Create folder to store results.
+        if to_save_dir is None:
+            to_save_dir = os.path.join(self.root_dir, self.model_name,
+                                       'inference_ckpt%d_sz%d' % (self.initial_step, self.image_size))
+
+        if not os.path.exists(to_save_dir):
+            os.makedirs(to_save_dir)
+
+        names = []
+        for d in path_to_folder:
+            names += glob(os.path.join(d, '*'))
+        names = [x for x in names if os.path.basename(x)[0] != '.']
+        names.sort()
+        for img_idx, img_path in enumerate(tqdm(names)):
+            img = scipy.misc.imread(img_path, mode='RGB')
+            img_shape = img.shape[:2]
+
+            # Resize the smallest side of the image to the self.image_size
+            alpha = float(self.image_size) / float(min(img_shape))
+            img = scipy.misc.imresize(img, size=alpha)
+            img = np.expand_dims(img, axis=0)
+
+            img = self.sess.run(
+                self.output_photo,
+                feed_dict={
+                    self.input_photo: normalize_arr_of_imgs(img),
+                })
+
+            img = img[0]
+            img = denormalize_arr_of_imgs(img)
+            if resize_to_original:
+                img = scipy.misc.imresize(img, size=img_shape)
+            else:
+                pass
+            img_name = os.path.basename(img_path)
+            #@STCGoal HERE TO APPEND SUFFIX TO FILE
+            scipy.misc.imsave(os.path.join(to_save_dir, img_name[:-4] + file_suffix +".jpg"), img)
+
+        print("Inference is finished.")
+
+    def save(self, step, is_long=False):
+        if not os.path.exists(self.checkpoint_dir):
+            os.makedirs(self.checkpoint_dir)
+        if is_long:
+            self.saver_long.save(self.sess,
+                                 os.path.join(self.checkpoint_long_dir, self.model_name+'_%d.ckpt' % step),
+                                 global_step=step)
+        else:
+            self.saver.save(self.sess,
+                            os.path.join(self.checkpoint_dir, self.model_name + '_%d.ckpt' % step),
+                            global_step=step)
+
+    def load(self, checkpoint_dir, ckpt_nmbr=None):
+        if ckpt_nmbr:
+            if len([x for x in os.listdir(checkpoint_dir) if ("ckpt-" + str(ckpt_nmbr)) in x]) > 0:
+                print(" [*] Reading checkpoint %d from folder %s." % (ckpt_nmbr, checkpoint_dir))
+                ckpt_name = [x for x in os.listdir(checkpoint_dir) if ("ckpt-" + str(ckpt_nmbr)) in x][0]
+                ckpt_name = '.'.join(ckpt_name.split('.')[:-1])
+                self.initial_step = ckpt_nmbr
+                print("Load checkpoint %s. Initial step: %s." % (ckpt_name, self.initial_step))
+                self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
+                return True
+            else:
+                return False
+        else:
+            print(" [*] Reading latest checkpoint from folder %s." % (checkpoint_dir))
+            ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
+            if ckpt and ckpt.model_checkpoint_path:
+                ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
+                self.initial_step = int(ckpt_name.split("_")[-1].split(".")[0])
+                print("Load checkpoint %s. Initial step: %s." % (ckpt_name, self.initial_step))
+                self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
+                return True
+            else:
+                return False

module.py

@@ -0,0 +1,246 @@
+# Copyright (C) 2018  Artsiom Sanakoyeu and Dmytro Kotovenko
+#
+# This file is part of Adaptive Style Transfer
+#
+# Adaptive Style Transfer is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Adaptive Style Transfer is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+from __future__ import division
+from ops import *
+
+
+def encoder(image, options, reuse=True, name="encoder"):
+    """
+    Args:
+        image: input tensor, must have
+        options: options defining number of kernels in conv layers
+        reuse: to create new encoder or use existing
+        name: name of the encoder
+
+    Returns: Encoded image.
+    """
+
+    with tf.variable_scope(name):
+        if reuse:
+            tf.get_variable_scope().reuse_variables()
+        else:
+            assert tf.get_variable_scope().reuse is False
+        image = instance_norm(input=image,
+                              is_training=options.is_training,
+                              name='g_e0_bn')
+        c0 = tf.pad(image, [[0, 0], [15, 15], [15, 15], [0, 0]], "REFLECT")
+        c1 = tf.nn.relu(instance_norm(input=conv2d(c0, options.gf_dim, 3, 1, padding='VALID', name='g_e1_c'),
+                                      is_training=options.is_training,
+                                      name='g_e1_bn'))
+        c2 = tf.nn.relu(instance_norm(input=conv2d(c1, options.gf_dim, 3, 2, padding='VALID', name='g_e2_c'),
+                                      is_training=options.is_training,
+                                      name='g_e2_bn'))
+        c3 = tf.nn.relu(instance_norm(conv2d(c2, options.gf_dim * 2, 3, 2, padding='VALID', name='g_e3_c'),
+                                      is_training=options.is_training,
+                                      name='g_e3_bn'))
+        c4 = tf.nn.relu(instance_norm(conv2d(c3, options.gf_dim * 4, 3, 2, padding='VALID', name='g_e4_c'),
+                                      is_training=options.is_training,
+                                      name='g_e4_bn'))
+        c5 = tf.nn.relu(instance_norm(conv2d(c4, options.gf_dim * 8, 3, 2, padding='VALID', name='g_e5_c'),
+                                      is_training=options.is_training,
+                                      name='g_e5_bn'))
+        return c5
+
+
+def decoder(features, options, reuse=True, name="decoder"):
+    """
+    Args:
+        features: input tensor, must have
+        options: options defining number of kernels in conv layers
+        reuse: to create new decoder or use existing
+        name: name of the encoder
+
+    Returns: Decoded image.
+    """
+
+    with tf.variable_scope(name):
+        if reuse:
+            tf.get_variable_scope().reuse_variables()
+        else:
+            assert tf.get_variable_scope().reuse is False
+
+        def residule_block(x, dim, ks=3, s=1, name='res'):
+            p = int((ks - 1) / 2)
+            y = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], "REFLECT")
+            y = instance_norm(conv2d(y, dim, ks, s, padding='VALID', name=name+'_c1'), name+'_bn1')
+            y = tf.pad(tf.nn.relu(y), [[0, 0], [p, p], [p, p], [0, 0]], "REFLECT")
+            y = instance_norm(conv2d(y, dim, ks, s, padding='VALID', name=name+'_c2'), name+'_bn2')
+            return y + x
+
+        # Now stack 9 residual blocks
+        num_kernels = features.get_shape().as_list()[-1]
+        r1 = residule_block(features, num_kernels, name='g_r1')
+        r2 = residule_block(r1, num_kernels, name='g_r2')
+        r3 = residule_block(r2, num_kernels, name='g_r3')
+        r4 = residule_block(r3, num_kernels, name='g_r4')
+        r5 = residule_block(r4, num_kernels, name='g_r5')
+        r6 = residule_block(r5, num_kernels, name='g_r6')
+        r7 = residule_block(r6, num_kernels, name='g_r7')
+        r8 = residule_block(r7, num_kernels, name='g_r8')
+        r9 = residule_block(r8, num_kernels, name='g_r9')
+
+        # Decode image.
+        d1 = deconv2d(r9, options.gf_dim * 8, 3, 2, name='g_d1_dc')
+        d1 = tf.nn.relu(instance_norm(input=d1,
+                                      name='g_d1_bn',
+                                      is_training=options.is_training))
+
+        d2 = deconv2d(d1, options.gf_dim * 4, 3, 2, name='g_d2_dc')
+        d2 = tf.nn.relu(instance_norm(input=d2,
+                                      name='g_d2_bn',
+                                      is_training=options.is_training))
+
+        d3 = deconv2d(d2, options.gf_dim * 2, 3, 2, name='g_d3_dc')
+        d3 = tf.nn.relu(instance_norm(input=d3,
+                                      name='g_d3_bn',
+                                      is_training=options.is_training))
+
+        d4 = deconv2d(d3, options.gf_dim, 3, 2, name='g_d4_dc')
+        d4 = tf.nn.relu(instance_norm(input=d4,
+                                      name='g_d4_bn',
+                                      is_training=options.is_training))
+
+        d4 = tf.pad(d4, [[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT")
+        pred = tf.nn.sigmoid(conv2d(d4, 3, 7, 1, padding='VALID', name='g_pred_c'))*2. - 1.
+        return pred
+
+
+def discriminator(image, options, reuse=True, name="discriminator"):
+    """
+    Discriminator agent, that provides us with information about image plausibility at
+    different scales.
+    Args:
+        image: input tensor
+        options: options defining number of kernels in conv layers
+        reuse: to create new discriminator or use existing
+        name: name of the discriminator
+
+    Returns:
+        Image estimates at different scales.
+    """
+    with tf.variable_scope(name):
+        if reuse:
+            tf.get_variable_scope().reuse_variables()
+        else:
+            assert tf.get_variable_scope().reuse is False
+
+        h0 = lrelu(instance_norm(conv2d(image, options.df_dim * 2, ks=5, name='d_h0_conv'),
+                   name='d_bn0'))
+        h0_pred = conv2d(h0, 1, ks=5, s=1, name='d_h0_pred', activation_fn=None)
+
+        h1 = lrelu(instance_norm(conv2d(h0, options.df_dim * 2, ks=5, name='d_h1_conv'),
+                                 name='d_bn1'))
+        h1_pred = conv2d(h1, 1, ks=10, s=1, name='d_h1_pred', activation_fn=None)
+
+        h2 = lrelu(instance_norm(conv2d(h1, options.df_dim * 4, ks=5, name='d_h2_conv'),
+                                 name='d_bn2'))
+
+        h3 = lrelu(instance_norm(conv2d(h2, options.df_dim * 8, ks=5, name='d_h3_conv'),
+                                 name='d_bn3'))
+        h3_pred = conv2d(h3, 1, ks=10, s=1, name='d_h3_pred', activation_fn=None)
+
+        h4 = lrelu(instance_norm(conv2d(h3, options.df_dim * 8, ks=5, name='d_h4_conv'),
+                                 name='d_bn4'))
+
+        h5 = lrelu(instance_norm(conv2d(h4, options.df_dim * 16, ks=5, name='d_h5_conv'),
+                                 name='d_bn5'))
+        h5_pred = conv2d(h5, 1, ks=6, s=1, name='d_h5_pred', activation_fn=None)
+
+        h6 = lrelu(instance_norm(conv2d(h5, options.df_dim * 16, ks=5, name='d_h6_conv'),
+                                 name='d_bn6'))
+        h6_pred = conv2d(h6, 1, ks=3, s=1, name='d_h6_pred', activation_fn=None)
+
+        return {"scale_0": h0_pred,
+                "scale_1": h1_pred,
+                "scale_3": h3_pred,
+                "scale_5": h5_pred,
+                "scale_6": h6_pred}
+
+
+# ====== Define different types of losses applied to discriminator's output. ====== #
+
+def abs_criterion(in_, target):
+    return tf.reduce_mean(tf.abs(in_ - target))
+
+
+def mae_criterion(in_, target):
+    return tf.reduce_mean(tf.abs(in_-target))
+
+def mse_criterion(in_, target):
+    return tf.reduce_mean((in_-target)**2)
+
+def sce_criterion(logits, labels):
+    return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))
+
+
+def reduce_spatial_dim(input_tensor):
+    """
+    Since labels and discriminator outputs are of different shapes (and even ranks)
+    we should write a routine to deal with that.
+    Args:
+        input: tensor of shape [batch_size, spatial_resol_1, spatial_resol_2, depth]
+    Returns:
+        tensor of shape [batch_size, depth]
+    """
+    input_tensor = tf.reduce_mean(input_tensor=input_tensor, axis=1)
+    input_tensor = tf.reduce_mean(input_tensor=input_tensor, axis=1)
+    return input_tensor
+
+
+def add_spatial_dim(input_tensor, dims_list, resol_list):
+    """
+        Appends dimensions mentioned in dims_list resol_list times. S
+        Args:
+            input: tensor of shape [batch_size, depth0]
+            dims_list: list of integers with position of new  dimensions to append.
+            resol_list: list of integers with corresponding new dimensionalities for each dimension.
+        Returns:
+            tensor of new shape
+        """
+    for dim, res in zip(dims_list, resol_list):
+
+        input_tensor = tf.expand_dims(input=input_tensor,  axis=dim)
+        input_tensor = tf.concat(values=[input_tensor]*res, axis=dim)
+    return input_tensor
+
+
+def repeat_scalar(input_tensor, shape):
+    """
+    Repeat scalar values.
+    :param input_tensor: tensor of shape [batch_size, 1]
+    :param shape: new_shape of the element of the tensor
+    :return: tensor of the shape [batch_size, *shape] with elements repeated.
+    """
+    with tf.control_dependencies([tf.assert_equal(tf.shape(input_tensor)[1], 1)]):
+        batch_size = tf.shape(input_tensor)[0]
+    input_tensor = tf.tile(input_tensor, tf.stack(values=[1, tf.reduce_prod(shape)], axis=0))
+    input_tensor = tf.reshape(input_tensor, tf.concat(values=[[batch_size], shape, [1]], axis=0))
+    return input_tensor
+
+
+def transformer_block(input_tensor, kernel_size=10):
+    """
+    This is a simplified version of transformer block described in our paper
+    https://arxiv.org/abs/1807.10201.
+    Args:
+        input_tensor: Image(or tensor of rank 4) we want to transform.
+        kernel_size: Size of kernel we apply to the input_tensor.
+    Returns:
+        Transformed tensor
+    """
+    return slim.avg_pool2d(inputs=input_tensor, kernel_size=kernel_size, stride=1, padding='SAME')

ops.py

@@ -0,0 +1,84 @@
+# Copyright (C) 2018  Artsiom Sanakoyeu and Dmytro Kotovenko
+#
+# This file is part of Adaptive Style Transfer
+#
+# Adaptive Style Transfer is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Adaptive Style Transfer is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+import math
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+from tensorflow.python.framework import ops
+import cv2
+
+import tensorflow.contrib.layers as tflayers
+
+from utils import *
+
+
+def batch_norm(input, is_training=True, name="batch_norm"):
+    x = tflayers.batch_norm(inputs=input,
+                            scale=True,
+                            is_training=is_training,
+                            trainable=True,
+                            reuse=None)
+    return x
+
+
+def instance_norm(input, name="instance_norm", is_training=True):
+    with tf.variable_scope(name):
+        depth = input.get_shape()[3]
+        scale = tf.get_variable("scale", [depth], initializer=tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32))
+        offset = tf.get_variable("offset", [depth], initializer=tf.constant_initializer(0.0))
+        mean, variance = tf.nn.moments(input, axes=[1, 2], keep_dims=True)
+        epsilon = 1e-5
+        inv = tf.rsqrt(variance + epsilon)
+        normalized = (input-mean)*inv
+        return scale*normalized + offset
+
+
+def conv2d(input_, output_dim, ks=4, s=2, stddev=0.02, padding='SAME', name="conv2d", activation_fn=None):
+    with tf.variable_scope(name):
+        return slim.conv2d(input_, output_dim, ks, s, padding=padding, activation_fn=activation_fn,
+                           weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
+                           biases_initializer=None)
+
+
+def deconv2d(input_, output_dim, ks=4, s=2, stddev=0.02, name="deconv2d"):
+    # Upsampling procedure, like suggested in this article:
+    # https://distill.pub/2016/deconv-checkerboard/. At first upsample
+    # tensor like an image and then apply convolutions.
+    with tf.variable_scope(name):
+        input_ = tf.image.resize_images(images=input_,
+                                        size=tf.shape(input_)[1:3] * s,
+                                        method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)  # That is optional
+        return conv2d(input_=input_, output_dim=output_dim, ks=ks, s=1, padding='SAME')
+
+
+def lrelu(x, leak=0.2, name="lrelu"):
+    return tf.maximum(x, leak*x)
+
+
+def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
+
+    with tf.variable_scope(scope or "Linear"):
+        matrix = tf.get_variable("Matrix", [input_.get_shape()[-1], output_size], tf.float32,
+                                 tf.random_normal_initializer(stddev=stddev))
+        bias = tf.get_variable("bias", [output_size],
+                               initializer=tf.constant_initializer(bias_start))
+        if with_w:
+            return tf.matmul(input_, matrix) + bias, matrix, bias
+        else:
+            return tf.matmul(input_, matrix) + bias
+

prepare_dataset.py

@@ -0,0 +1,159 @@
+# Copyright (C) 2018  Artsiom Sanakoyeu and Dmytro Kotovenko
+#
+# This file is part of Adaptive Style Transfer
+#
+# Adaptive Style Transfer is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Adaptive Style Transfer is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+from __future__ import print_function
+import pandas as pd
+import numpy as np
+import os
+import time
+from tqdm import tqdm
+import scipy.misc
+import utils
+import random
+
+
+class ArtDataset():
+    def __init__(self, path_to_art_dataset):
+
+        self.dataset = [os.path.join(path_to_art_dataset, x) for x in os.listdir(path_to_art_dataset)]
+        print("Art dataset contains %d images." % len(self.dataset))
+
+    def get_batch(self, augmentor, batch_size=1):
+        """
+        Reads data from dataframe data containing path to images in column 'path' and, in case of dataframe,
+         also containing artist name, technique name, and period of creation for given artist.
+         In case of content images we have only the 'path' column.
+        Args:
+            augmentor: Augmentor object responsible for augmentation pipeline
+            batch_size: size of batch
+        Returns:
+            dictionary with fields: image
+        """
+
+        batch_image = []
+
+        for _ in range(batch_size):
+            image = scipy.misc.imread(name=random.choice(self.dataset), mode='RGB')
+
+            if max(image.shape) > 1800.:
+                image = scipy.misc.imresize(image, size=1800. / max(image.shape))
+            if max(image.shape) < 800:
+                # Resize the smallest side of the image to 800px
+                alpha = 800. / float(min(image.shape))
+                if alpha < 4.:
+                    image = scipy.misc.imresize(image, size=alpha)
+                    image = np.expand_dims(image, axis=0)
+                else:
+                    image = scipy.misc.imresize(image, size=[800, 800])
+
+            if augmentor:
+                batch_image.append(augmentor(image).astype(np.float32))
+            else:
+                batch_image.append((image).astype(np.float32))
+        # Now return a batch in correct form
+        batch_image = np.asarray(batch_image)
+
+        return {"image": batch_image}
+
+    def initialize_batch_worker(self, queue, augmentor, batch_size=1, seed=228):
+        np.random.seed(seed)
+        while True:
+            batch = self.get_batch(augmentor=augmentor, batch_size=batch_size)
+            queue.put(batch)
+
+
+class PlacesDataset():
+    categories_names = \
+        ['/a/abbey', '/a/arch', '/a/amphitheater', '/a/aqueduct', '/a/arena/rodeo', '/a/athletic_field/outdoor',
+         '/b/badlands', '/b/balcony/exterior', '/b/bamboo_forest', '/b/barn', '/b/barndoor', '/b/baseball_field',
+         '/b/basilica', '/b/bayou', '/b/beach', '/b/beach_house', '/b/beer_garden', '/b/boardwalk', '/b/boathouse',
+         '/b/botanical_garden', '/b/bullring', '/b/butte', '/c/cabin/outdoor', '/c/campsite', '/c/campus',
+         '/c/canal/natural', '/c/canal/urban', '/c/canyon', '/c/castle', '/c/church/outdoor', '/c/chalet',
+         '/c/cliff', '/c/coast', '/c/corn_field', '/c/corral', '/c/cottage', '/c/courtyard', '/c/crevasse',
+         '/d/dam', '/d/desert/vegetation', '/d/desert_road', '/d/doorway/outdoor', '/f/farm', '/f/fairway',
+         '/f/field/cultivated', '/f/field/wild', '/f/field_road', '/f/fishpond', '/f/florist_shop/indoor',
+         '/f/forest/broadleaf', '/f/forest_path', '/f/forest_road', '/f/formal_garden', '/g/gazebo/exterior',
+         '/g/glacier', '/g/golf_course', '/g/greenhouse/indoor', '/g/greenhouse/outdoor', '/g/grotto', '/g/gorge',
+         '/h/hayfield', '/h/herb_garden', '/h/hot_spring', '/h/house', '/h/hunting_lodge/outdoor', '/i/ice_floe',
+         '/i/ice_shelf', '/i/iceberg', '/i/inn/outdoor', '/i/islet', '/j/japanese_garden', '/k/kasbah',
+         '/k/kennel/outdoor', '/l/lagoon', '/l/lake/natural', '/l/lawn', '/l/library/outdoor', '/l/lighthouse',
+         '/m/mansion', '/m/marsh', '/m/mausoleum', '/m/moat/water', '/m/mosque/outdoor', '/m/mountain',
+         '/m/mountain_path', '/m/mountain_snowy', '/o/oast_house', '/o/ocean', '/o/orchard', '/p/park',
+         '/p/pasture', '/p/pavilion', '/p/picnic_area', '/p/pier', '/p/pond', '/r/raft', '/r/railroad_track',
+         '/r/rainforest', '/r/rice_paddy', '/r/river', '/r/rock_arch', '/r/roof_garden', '/r/rope_bridge',
+         '/r/ruin', '/s/schoolhouse', '/s/sky', '/s/snowfield', '/s/swamp', '/s/swimming_hole',
+         '/s/synagogue/outdoor', '/t/temple/asia', '/t/topiary_garden', '/t/tree_farm', '/t/tree_house',
+         '/u/underwater/ocean_deep', '/u/utility_room', '/v/valley', '/v/vegetable_garden', '/v/viaduct',
+         '/v/village', '/v/vineyard', '/v/volcano', '/w/waterfall', '/w/watering_hole', '/w/wave',
+         '/w/wheat_field', '/z/zen_garden', '/a/alcove', '/a/apartment-building/outdoor', '/a/artists_loft',
+         '/b/building_facade', '/c/cemetery']
+    categories_names = [x[1:] for x in categories_names]
+
+    def __init__(self, path_to_dataset):
+        self.dataset = []
+        for category_idx, category_name in enumerate(tqdm(self.categories_names)):
+            print(category_name, category_idx)
+            if os.path.exists(os.path.join(path_to_dataset, category_name)):
+                for file_name in tqdm(os.listdir(os.path.join(path_to_dataset, category_name))):
+                    self.dataset.append(os.path.join(path_to_dataset, category_name, file_name))
+            else:
+                print("Category %s can't be found in path %s. Skip it." %
+                      (category_name, os.path.join(path_to_dataset, category_name)))
+
+        print("Finished. Constructed Places2 dataset of %d images." % len(self.dataset))
+
+    def get_batch(self, augmentor, batch_size=1):
+        """
+        Generate bathes of images with attached labels(place category) in two different formats:
+        textual and one-hot-encoded.
+        Args:
+            augmentor: Augmentor object responsible for augmentation pipeline
+            batch_size: size of batch we return
+        Returns:
+            dictionary with fields: image
+        """
+
+        batch_image = []
+        for _ in range(batch_size):
+            image = scipy.misc.imread(name=random.choice(self.dataset), mode='RGB')
+            image = scipy.misc.imresize(image, size=2.)
+            image_shape = image.shape
+
+            if max(image_shape) > 1800.:
+                image = scipy.misc.imresize(image, size=1800. / max(image_shape))
+            if max(image_shape) < 800:
+                # Resize the smallest side of the image to 800px
+                alpha = 800. / float(min(image_shape))
+                if alpha < 4.:
+                    image = scipy.misc.imresize(image, size=alpha)
+                    image = np.expand_dims(image, axis=0)
+                else:
+                    image = scipy.misc.imresize(image, size=[800, 800])
+
+            batch_image.append(augmentor(image).astype(np.float32))
+
+        return {"image": np.asarray(batch_image)}
+
+    def initialize_batch_worker(self, queue, augmentor, batch_size=1, seed=228):
+        np.random.seed(seed)
+        while True:
+            batch = self.get_batch(augmentor=augmentor, batch_size=batch_size)
+            queue.put(batch)
+
+
+
+

server.py

@@ -0,0 +1,48 @@
+import os
+import numpy as np
+import tensorflow as tf
+from module import encoder, decoder
+from glob import glob
+import runway
+
+
+@runway.setup(options={'styleCheckpoint': runway.file(is_directory=True)})
+def setup(opts):
+    sess = tf.Session()
+    init_op = tf.global_variables_initializer()
+    sess.run(init_op)
+    with tf.name_scope('placeholder'):
+        input_photo = tf.placeholder(dtype=tf.float32,
+                                     shape=[1, None, None, 3],
+                                     name='photo')
+    input_photo_features = encoder(image=input_photo,
+                                   options={'gf_dim': 32},
+                                   reuse=False)
+    output_photo = decoder(features=input_photo_features,
+                           options={'gf_dim': 32},
+                           reuse=False)
+    saver = tf.train.Saver()
+    path = opts['styleCheckpoint']
+    model_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][0]
+    checkpoint_dir = os.path.join(path, model_name, 'checkpoint_long')
+    ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
+    ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
+    saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
+    return dict(sess=sess, input_photo=input_photo, output_photo=output_photo)
+
+
+@runway.command('stylize', inputs={'contentImage': runway.image}, outputs={'stylizedImage': runway.image})
+def stylize(model, inp):
+    img = inp['contentImage']
+    img = np.array(img)
+    img = img / 127.5 - 1.
+    img = np.expand_dims(img, axis=0)
+    img = model['sess'].run(model['output_photo'], feed_dict={model['input_photo']: img})
+    img = (img + 1.) * 127.5
+    img = img.astype('uint8')
+    img = img[0]
+    return dict(stylizedImage=img)
+
+
+if __name__ == '__main__':
+    runway.run()

test-gpu.py

@@ -0,0 +1,12 @@
+from tensorflow.keras import layers
+from tensorflow.keras import models
+model = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10, activation='softmax'))
+model.summary()

utils.py

@@ -0,0 +1,75 @@
+# Copyright (C) 2018  Artsiom Sanakoyeu and Dmytro Kotovenko
+#
+# This file is part of Adaptive Style Transfer
+#
+# Adaptive Style Transfer is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Adaptive Style Transfer is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+from __future__ import division
+import math
+import scipy.misc
+from scipy.ndimage.filters import gaussian_filter
+
+import numpy as np
+from ops import *
+import random
+import copy
+
+
+def save_batch(input_painting_batch, input_photo_batch, output_painting_batch, output_photo_batch, filepath):
+    """
+    Concatenates, processes and stores batches as image 'filepath'.
+    Args:
+        input_painting_batch: numpy array of size [B x H x W x C]
+        input_photo_batch: numpy array of size [B x H x W x C]
+        output_painting_batch: numpy array of size [B x H x W x C]
+        output_photo_batch: numpy array of size [B x H x W x C]
+        filepath: full name with path of file that we save
+
+    Returns:
+
+    """
+    def batch_to_img(batch):
+        return np.reshape(batch,
+                          newshape=(batch.shape[0]*batch.shape[1], batch.shape[2], batch.shape[3]))
+
+    inputs = np.concatenate([batch_to_img(input_painting_batch), batch_to_img(input_photo_batch)],
+                            axis=0)
+    outputs = np.concatenate([batch_to_img(output_painting_batch), batch_to_img(output_photo_batch)],
+                             axis=0)
+
+    to_save = np.concatenate([inputs,outputs], axis=1)
+    to_save = np.clip(to_save, a_min=0., a_max=255.).astype(np.uint8)
+
+    scipy.misc.imsave(filepath, arr=to_save)
+
+
+def normalize_arr_of_imgs(arr):
+    """
+    Normalizes an array so that the result lies in [-1; 1].
+    Args:
+        arr: numpy array of arbitrary shape and dimensions.
+    Returns:
+    """
+    return arr/127.5 - 1.
+    # return (arr - np.mean(arr)) / np.std(arr)
+
+
+def denormalize_arr_of_imgs(arr):
+    """
+    Inverse of the normalize_arr_of_imgs function.
+    Args:
+        arr: numpy array of arbitrary shape and dimensions.
+    Returns:
+    """
+    return (arr + 1.) * 127.5